ML/Dev Ops Systems Engineer at Zensors

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

06 May, 26

Salary

0.0

Posted On

05 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

DevOps, Automation Engineering, Linux Systems Internals, Networking, Infrastructure Orchestration, Kubernetes, CI/CD, Infrastructure as Code, Video Streaming, Monitoring Systems, Cloud Computing, Microservices, Security Scans, Static Code Analysis, High-Throughput Data Pipelines, Complex Systems Integration

Industry

Software Development

Description

Zensors is the spatial intelligence platform for the physical world. Our AI platform provides real-time insights—from airport queue times to office utilization—helping organizations make smarter operational decisions.Zensors is processing massive streams of video data 24/7 with human-level accuracy. To do this at scale, we rely on cutting-edge optimization to ensure our vision transformer and detection models run efficiently on both cloud and edge compute resources. You will play a pivotal role in advancing our infrastructure, scaling enterprise deployment workflows, and refining automation architectures to enable rapid iteration across the organization. This role requires deep technical expertise not just in cloud-native tools, but also in the foundational Linux systems and networking required to process high-throughput video data reliably and securely. Key Responsibilities Infrastructure & Automation Strategy: Drive the design and implementation of automated infrastructure deployment and validation workflows supporting our AI and computer vision initiatives. Video Pipeline Operations: Design, optimize, and manage the infrastructure specifically tailored for ingesting, processing, and analyzing real-time video streams at scale. You will ensure high throughput, low latency, and reliability for critical computer vision workloads. Systems & Networking Core: Maintain a strong systems foundation by managing high-performance Linux environments. You will architect and troubleshoot complex networking configurations (both cloud and edge) necessary for seamless video data transmission between cameras, processing nodes, and the cloud platform. Kubernetes & Orchestration: Create resilient automation pipelines, orchestrate complex Kubernetes-based environments, and ensure the seamless integration of diverse software components. CI/CD & Deployment: Design sophisticated CI/CD pipelines. Your scope will include automating infrastructure provisioning (potentially bare-metal-to-Kubernetes bring-up), deploying microservices utilizing Helm, and integrating security scans and static code analysis tools into the workflow. Reliability & Monitoring: Build comprehensive monitoring systems and automated alerting mechanisms tailored for AI/video workloads. You will diagnose and resolve complex build failures and production issues related to system resources or network bottlenecks. Collaboration & Scaling: Collaborate deeply with ML engineers to ensure validation readiness for new models and take ownership of scaling enterprise deployment workflows across the organization. Qualifications Professional Profile: A highly motivated and passionate professional with deep expertise in DevOps, automation engineering, Linux systems internals, advanced networking, and infrastructure orchestration. You must have a strong track record of technical execution, complex systems integration, and successful cross-team collaboration. Education & Experience: A BS, MS, or PhD in Computer Science or a related equivalent field, combined with 6+ years of applicable industry experience in DevOps or Systems Engineering. Other Technical Requirements Expert-level knowledge of Linux administration, kernel tuning, and system performance debugging. Strong understanding of networking protocols (TCP/IP, UDP, DNS, VPNs, firewalls) and container networking challenges (CNI, service mesh). Proven experience managing infrastructure for video streaming (e.g., RTSP, HLS, WebRTC) or similarly high-throughput, real-time data pipelines. Deep expertise in Kubernetes (managing clusters, Helm charts, orchestration). Strong background in CI/CD toolchains (e.g., Jenkins, GitLab CI, ArgoCD) and Infrastructure as Code (e.g., Terraform, Ansible). Experience in NixOS environments and package management as well as virtualization environments

Responsibilities

The role involves driving the design and implementation of automated infrastructure deployment and managing video pipeline operations for real-time video streams. Additionally, the engineer will collaborate with ML engineers to scale enterprise deployment workflows.